Comment: Hydrodynamic and Atmospheric Conditions in a Volcanic Caldera: A Comprehensive Dataset at Deception Island, Antarctica



This study presents the first 16-year (2005–2020) high-resolution integrated atmospheric-hydrodynamic dataset for Deception Island, a volcanic caldera in Antarctica, combining nested WRF atmospheric simulations (1 km grid) and DELFT3D hydrodynamic modeling (15×25 m grid). The dataset addresses critical knowledge gaps in Antarctic coastal systems by capturing spatial-seasonal variability and extreme events (e.g., storm-driven waves >4 m).



The integration of WRF downscaling (validated against in-situ weather stations) and DELFT3D (calibrated with tidal harmonics) enables robust analysis of wind-wave-current interactions during extreme events. The dataset supports diverse applications, including glacial meltwater transport, nutrient dynamics, and ecosystem resilience, aligning with Antarctic conservation priorities.

1. The correlation coefficients for temperature (R=0.86–0.99) and pressure (R=0.99) are robust, but lower correlations for precipitation (R=0.43) and humidity (R=0.57) require deeper analysis.

Precipitation is linked to the micro-physics parameterization and both a long and accurate observation time-series and an in-depth sensitivity analysis in polar areas, would be needed to accurately calibrate model precipitation. In fact, precipitation in areas characterized by polar climate could need the development and/or the specific validation of the parameterization scheme to improve reliability in precipitation simulations. Furthermore, for high-resolution simulations such, punctual comparison between observation and forecast is affected by the so called -double penalty- effect, that result in degradation of traditional standard verfication index (Cassola et al., 2015). A more sophisticated analysis could required, for example, an object oriented validation of the simulations, based on the direct comparison between observed and forecast precipitation fields (Ferrari et al., 2023). Likewise, humidity can partially be influenced by precipitation leading to lower correlations. Additionally, the reliability of observation of solid precipitation, like snow, could be strongly affected by wind and with this region being characterized by both strong winds and solid precipitation, inaccuracies between observations and model could be detected. For this, lower correlations are expected for variables such as precipitation and humidity than temperature or pressure. Nonetheless, considering the 1km grid for the WRF model, we believe the correlations obtained for all variables are adequate to use for future analysis. This has been further clarified in the revised version of the manuscript.



Cassola, F., Ferrari, F. and Mazzino, A., 2015. Numerical simulations of Mediterranean heavy precipitation events with the WRF model: A verification exercise using different approaches. Atmospheric Research, 164, pp.210-225.

Ferrari, F., Maggioni, E., Perotto, A., Salerno, R. and Giudici, M., 2023. Cascade sensitivity tests to model deep convective systems in complex orography with WRF. Atmospheric Research, 295, p.106964.

1. Tidal constituents (M2, S2) show excellent agreement with historical data, yet the absence of satellite altimetry validation limits confidence in open-ocean boundary conditions.

We thank the reviewer for this valuable comment. Unfortunately, historical satellite altimetry data with sufficient spatial and temporal resolution are not available for Deception Island, which prevents us from using such data for model calibration. However, our study focuses primarily on the hydrodynamic behaviour within the island's caldera rather than in the open ocean. Model calibration was performed using in-situ observations from previously published studies, providing a robust validation of the numerical model for our area of interest. Furthermore, even if satellite altimetry with a sufficient resolution was available, it would require additional calibration and validation against in-situ measurements to be fully reliable in this particular environment. We have clarified this point in the revised manuscript.

1. Missing hydrodynamic data (2005–2020) due to computational errors or Copernicus boundary gaps need explicit justification. The 10×10 m bathymetry may inadequately resolve narrow channels. Higher-resolution terrain data should be tested to assess sensitivity.

We appreciate the reviewer’s comment regarding the missing hydrodynamic data and the resolution of the bathymetric input. Regarding the missing hydrodynamic data during the 2005–2020 period, the gaps are mainly due to occasional computational instabilities in the numerical hydrodynamic model and missing input data from the Copernicus Marine Service used as boundary conditions. To prevent introducing artificial variability, simulations were halted during such periods, and no gap-filling or synthetic reconstruction was applied, preserving the physical consistency and reliability of the dataset. Regarding the bathymetric resolution, although 10×10 m data were used, our study's focus is on basin-scale atmospheric and hydrodynamic variability, not on detailed geomorphological or fine-scale channel flows. Therefore, the current resolution is considered appropriate for the study objectives. Nevertheless, we acknowledge the importance of high-resolution bathymetric data for studies specifically targeting channel dynamics, and we have included this limitation in the revised manuscript.

“The missing hydrodynamic data for certain periods between 2005 and 2020 are mainly due to computational instabilities or the unavailability of boundary condition data from the Copernicus Marine Service. No artificial gap-filling has been applied to maintain the physical reliability of the dataset. Although the bathymetric data used have a horizontal resolution of 10×10 m, this is considered sufficient for the study objectives, which focus on basin-scale hydrodynamic and atmospheric variability. Future studies specifically targeting detailed channel dynamics may benefit from the use of higher resolution terrain data.“

1. The seasonal wind speed maxima (>10 m/s in winter) and wave height contrasts (summer: 0.4 m vs. winter: 2.5 m) are well-documented. However, linking these trends to broader climate signals would enhance relevance.

We thank the reviewer for the insightful suggestion to link observed seasonal variability to broader climatic signals. In response, we have expanded the manuscript by explicitly discussing the relationship between local wind speed and large-scale climate variability, specifically the Southern Annular Mode (SAM) and the El Niño–Southern Oscillation (ENSO). We have added a new paragraph in the results and discussion section, complemented by a new figure (Figure X) that compares the monthly mean wind speed at Deception Island with the SAM index over the period 2005–2020. This addition highlights the influence of positive SAM phases on stronger wind conditions observed locally. Furthermore, we briefly discuss the potential effects of ENSO phases on atmospheric and hydrodynamic variability in the region, suggesting directions for future research. We believe these additions strengthen the broader climatological relevance of our work and directly address the reviewer’s recommendation.

“Figure X.-Monthly wind speed (blue line) and Southern Annular Mode (SAM) index (red dashed line) at Deception Island from 2005 to 2020, illustrating the relationship between local wind variability and large-scale climate patterns.



The observed seasonal variability in wind speed and wave height at Deception Island, characterized by higher values during austral winter, is consistent with broader climate patterns affecting the Southern Ocean. In particular, phases of the Southern Annular Mode (SAM) are known to modulate the intensity and persistence of westerly winds over the South Shetland Islands region (e.g., Marshall, 2003). Figure X shows the comparison between the monthly mean wind speed at Deception Island and the Southern Annular Mode (SAM) index over the period 2005-2020. The SAM index describes the difference in zonal mean sea level pressure between approximately 40°S and 65°S, capturing the variability of the westerly wind belt that surrounds Antarctica. Positive SAM phases are associated with stronger and poleward shifting westerlies, while negative phases indicate weakening and equatorward shifting. The figure shows a general correspondence between periods of positive SAM and higher wind speeds at Deception Island, suggesting that local atmospheric conditions are modulated, at least in part, by broader climatic patterns. This relationship highlights the potential influence of large-scale climate variability on the hydrodynamic and atmospheric processes studied in this work.



In addition to the Southern Annular Mode (SAM), the El Niño-Southern Oscillation (ENSO) also influences atmospheric and oceanographic conditions in the Antarctic Peninsula region. El Niño events are generally associated with weaker westerly winds and reduced storm activity, while La Niña phases tend to increase wind strength and hydrodynamic forcing. Although the present analysis focuses primarily on local variability, future studies could explore the relationship between ENSO phases and the patterns observed at Deception Island.“

1. The storm case study highlights wave-current coupling but lacks analysis of climate-driven frequency changes.

We thank the reviewer for this important observation. The storm case study was selected to illustrate the coupled dynamics of wind, waves, and currents during an extreme event. While a comprehensive trend analysis of storm frequency changes was beyond the scope of the present work, we agree that investigating potential climate-driven changes in the occurrence of such events is highly relevant. In response, we have added a discussion point acknowledging this limitation and suggesting that future studies could integrate climate indices and longer-term trend analyses to assess changes in the frequency and intensity of extreme hydrodynamic conditions in the region. This would enhance the predictive power of the dataset in the context of ongoing climate change.



"While this case study illustrates the dynamic response of the system to an extreme weather event, the dataset also provides an opportunity for future analyses on the frequency and intensity of such events in relation to large-scale climate variability. Investigating long-term trends and their potential links to climate change, including the role of SAM and ENSO, could offer valuable insights into the evolving risk of extreme hydrodynamic conditions in the Antarctic Peninsula region."

1. Incorporate satellite altimetry or Argo float data to validate hydrodynamic outputs in open-ocean regions.

We appreciate the reviewer’s suggestion to incorporate satellite altimetry or Argo float data to validate hydrodynamic outputs in open-ocean regions. However, due to the geographic and environmental constraints of the Southern Ocean, such high-resolution data are either scarce or unavailable for the Deception Island region during the simulation period (2005–2020). Specifically, access to Argo float data for this region and period reveals that measurements are irregular both spatially and temporally, with significant data gaps, especially under ice cover. Moreover, the available Argo data, when accessible, refer to undefined depths, and are not suitable for surface water level validation; similarly, attempts to extract pressure-related values at the necessary resolution and time range (2005–2020) have been unsuccessful. Additionally, our study focuses on the internal hydrodynamic processes within the Deception Island caldera rather than open-ocean dynamics, making these external datasets less relevant to our specific research objectives.

Furthermore, we are not specialists in the processing and interpretation of satellite altimetry or Argo float data, and using such datasets without appropriate expertise could introduce additional sources of uncertainty. Our calibration and validation approach, based on in-situ observational data from previous published studies, provides a reliable and robust basis for assessing model performance within the scope of this work. This point has been clarified in the revised manuscript.



This study delivers a pioneering dataset for Antarctic volcanic caldera systems, with significant potential for cross-disciplinary research. Addressing validation gaps and climate linkages will solidify its impact.